{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9c418f35",
   "metadata": {},
   "source": [
    "# Accessing NEP descriptors\n",
    "\n",
    "This tutorial demonstrates the basic functionality available for the analysis of NEP models.\n",
    "Specifically, the interface exposes convenience functions for calculating per-atom descriptors, energies, forces, virials/stresses, dipoles and latent space representations for [Atoms objects](https://wiki.fysik.dtu.dk/ase/ase/atoms.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb20f790",
   "metadata": {},
   "source": [
    "All models and structures required for running this and the other tutorial notebooks can be obtained from [Zenodo](https://zenodo.org/record/10658778).\n",
    "The files are also available in the `tutorials/` folder in the [GitLab repository](https://gitlab.com/materials-modeling/calorine/-/tree/master/tutorials)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfd330c0",
   "metadata": {},
   "source": [
    "We start by creating an `Atoms` object, which will serve as our prototype system for the extent of this tutorial, and by importing the `get_descriptors`, `get_potential_forces_and_virials`, `get_dipole` and `get_latent_space` functions from the `calorine.nep` submodule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "83a90034",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ase.io import read\n",
    "from ase.build import bulk\n",
    "import matplotlib.pyplot as plt\n",
    "from calorine.nep import get_descriptors, \\\n",
    "                         get_potential_forces_and_virials, \\\n",
    "                         get_dipole, \\\n",
    "                         get_latent_space\n",
    "\n",
    "structure = bulk('PbTe', crystalstructure='rocksalt', a=6.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87793a0c",
   "metadata": {},
   "source": [
    "We can now compute the descriptors, energies, forces and virials for our system by calling `get_descriptors` and `get_potential_forces_and_virials`.\n",
    "To this end, we pass the atomic structure together with the path to a NEP model file.\n",
    "Here, we use a [NEP4 model for PbTe](https://github.com/brucefan1983/GPUMD/blob/ad07f7a031c87a9f235e54035d179a118ab2ff9a/examples/11_NEP_potential_PbTe/nep.txt)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fd406810",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of descriptors: (2, 30)\n",
      "Energy (eV):\n",
      " [-1.94265756 -5.72050595]\n",
      "Forces (eV/Å):\n",
      " [[ 9.43689571e-16 -2.08961602e-16 -4.73106562e-16]\n",
      " [-9.50628465e-16  2.08961602e-16  4.73106562e-16]]\n",
      "Virials (eV):\n",
      " [[-9.29170315e-01 -1.43931046e-16 -3.65311983e-17 -1.56074110e-16\n",
      "  -9.29170315e-01  5.89805982e-17 -3.65311983e-17  5.72458747e-17\n",
      "  -9.29170315e-01]\n",
      " [ 1.08945431e-01 -5.20081776e-16 -1.04008905e-16 -5.20081776e-16\n",
      "   1.08945431e-01  1.31913488e-16 -1.21356140e-16  1.24974594e-16\n",
      "   1.08945431e-01]]\n"
     ]
    }
   ],
   "source": [
    "descriptors = get_descriptors(structure, model_filename='nep-PbTe.txt')\n",
    "\n",
    "per_atom_energies, forces, virials = \\\n",
    "    get_potential_forces_and_virials(structure, model_filename='nep-PbTe.txt')\n",
    "\n",
    "print(f'Shape of descriptors: {descriptors.shape}')\n",
    "print(f'Energy (eV):\\n {per_atom_energies}')\n",
    "print(f'Forces (eV/Å):\\n {forces}')\n",
    "print(f'Virials (eV):\\n {virials}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dae0f087",
   "metadata": {},
   "source": [
    "`get_descriptors` can also be called without supplying a NEP model file, in which case a dummy NEP2 model will be generated on-the-fly.\n",
    "The dummy model has all parameter values set to 1.\n",
    "*Note that this model does not differentiate different atom species.*\n",
    "Thus, supplying your own trained NEP model file is recommended for most applications. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b83ea356",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of descriptors: (2, 52)\n"
     ]
    }
   ],
   "source": [
    "descriptors = get_descriptors(structure)\n",
    "print(f'Shape of descriptors: {descriptors.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e06362c",
   "metadata": {},
   "source": [
    "#### Accessing latent space representations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "979f664d",
   "metadata": {},
   "source": [
    "The latent space representation of a structure can be obtained using the function `get_latent_space`. The latent space is a matrix, with shape `(number_of_atoms, number_of_neurons)` where `number_of_neurons` is the number of neurons in the hidden layer in the neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "023bf538",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of latent space: (2, 30)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "structure = bulk('PbTe', crystalstructure='rocksalt', a=6.7)\n",
    "latent = get_latent_space(structure, model_filename='nep-PbTe.txt')\n",
    "\n",
    "print(f'Shape of latent space: {latent.shape}')\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(latent[0,:], label='Atom 0')\n",
    "ax.plot(latent[1,:], label='Atom 1')\n",
    "ax.set_xlabel('Neuron index')\n",
    "ax.set_ylabel('Activation')\n",
    "ax.legend(loc='best');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "vscode": {
   "interpreter": {
    "hash": "6be099dd3823815779ed5b2d986ab000320f08fb94733d934e13608f08061690"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
